Daytona: 16 Engineers Manage 5 AI Agents Each, No More Code

Key Takeaways

1At Daytona, each engineer manages an average of five AI agents and no longer codes

2The company, founded in 2023, has around 30 people and raised $24 million in a Series A in February 2026

3Ivan Burazin believes that managerial experience enhances effectiveness with AI agents but also requires technical skills

Why it matters : Daytona illustrates how the integration of AI agents allows for smaller human teams and adapts management practices.

The CEO of Daytona, Ivan Burazin, describes an organization where engineers oversee multiple AI agents and where hiring remains measured. He sees this as an extension of classic management practices, while also implementing technical safeguards.

Agents to Limit Hiring and Maintain Speed

Ivan Burazin indicates that hiring at Daytona is progressing very slowly, with each new addition adding complexity. He asserts that entrusting his teams with the management of several AI agents helps maintain a lean workforce. According to him, these agents reduce the communication needed within human teams and enable faster progress. At Daytona, each engineer manages an average of five AI agents, and he claims that no one codes anymore within the company.

$24 Million Raised and a Secure Sandbox Offering

In February 2026, Daytona raised $24 million in a Series A round led by FirstMark Capital. The company was founded in 2023 by Ivan Burazin along with Vedran Jukić and Goran Draganić. Its product consists of a secure sandbox infrastructure designed to run AI agents and AI-generated code in isolated environments.

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Organization: 30 People Across Two Continents and a Co-Founder CEO

Ivan Burazin is the CEO and co-founder of Daytona, an AI company based in New York. The company has about 30 employees, spread between the United States and Europe. It includes 16 engineers, who are at the core of the organization described by its leadership.

Supervising Agents Like Humans, with Technical Skills

Ivan Burazin argues that managing AI agents is similar to managing people and says he has encouraged his teams in recent months to create more of them. He observes that those who have previously supervised colleagues achieve better results with the agents because they know how to clarify objectives and the relationship between inputs and outputs. Conversely, he believes that engineers without managerial experience often implicitly expect the agent to understand the request and become frustrated when it does not. However, he clarifies that effectiveness is not reserved for people managers and adds that managers lacking technical skills are not useful; everyone must contribute to the concrete work. This approach comes after a phase referred to as Great Flattening, marked by reductions in middle management at companies like Meta, Amazon, and Google, and by roles where more and more leaders combine coding, product, and management.